Intelligent Adaptive Deep Neural Network-Based Signal Detection for Generalized Spatial Modulation Visible Light Communication Systems

Visible light communication (VLC) is a promising solution for indoor wireless access owing to its unlicensed optical spectrum and compatibility with existing light-emitting diode (LED) infrastructure. However, practical VLC systems suffer from channel variability, multipath interference, and partial blockages. To address these challenges, we propose an intelligent adaptive deep neural network (IADNN) framework for generalized spatial modulation (GSM)-based VLC systems. The proposed framework comprises three innovations: a residual ensemble DNN architecture to capture nonlinear channel effects, a dynamic weight fusion mechanism to stabilize inference, and a multi-scenario data augmentation strategy to enhance robustness. Simulation results demonstrate that the proposed framework achieves near-optimal bit error rate (BER) performance across a wide signal-to-noise ratio (SNR) range, outperforming conventional zero-forcing (ZF) and minimum mean square error (MMSE) detectors with BER reductions of up to 87% and 66%, respectively. At the practical operating point of 10^-3 BER, the IADNN incurs only a 0.5 dB SNR penalty versus the maximum-likelihood (ML) bound while cutting inference latency by 21 times. The ensemble strategy further lowers the coefficient of variation (CoV) by 58 % and improves reliability by 21 % over a single-DNN baseline. Although IADNN introduces moderate additional parameters, it offers a favorable complexity–performance trade-off, especially for large LED arrays where ML detection is computationally prohibitive. These properties make IADNN a practical enabler for real-time Light Fidelity (LiFi) hot-spots and dense Internet of Things (IoT) illumination networks in next-generation indoor environments.